library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)

coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major T.super markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v6_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	CD4.T.CXCL13	CD4.T.naive	CD4.T.reg	CD4.Th17	CD8.T.CXCL13	CD8.T.cytotoxic	CD8.T.ISG	Cycling.T.NK	dissociated	NK.CD56	NK.cytotoxic
1	CD4	CCR7	CD4	CCR6	CCL3	CCL4	IFI6	ASPM	BTG1	AREG	FCGR3A
2	CD40LG	CD4	FOXP3	IL4I1	CCL4L2	CCL5	IFIT1	CENPF	DNAJB1	FCER1G	FGFBP2
3	CTLA4	CD40LG	IL2RA	IL7R	CD8A	CD8A	IFIT2	HIST1H4C	DUSP1	GNLY	KLRD1
4	CXCL13	IL7R	TNFRSF4	KLRB1	CRTAM	CD8B	IFIT3	HMGB2	EGR1	KLRC1	KLRF1
5	FKBP5	KLF2	TRAC	LST1	CXCL13	CRTAM	ISG15	MKI67	FOS	KRT81	PRF1
6	IL6ST	LTB		LTB	FABP5	CST7	MX1	STMN1	FOSB	TRDC	SPON2
7	ITM2A	TCF7		TNFSF13B	GZMB	DTHD1	MX2	TOP2A	HSPA1A	TYROBP
8	MAF	TPT1			HAVCR2	GZMA	RSAD2	TUBA1B	HSPA1B	XCL1
9	NMB				IFNG	GZMH		TUBB	HSPA6	XCL2
10	NR3C1				LAG3	GZMK		TYMS	JUN
11	PDCD1				MIR155HG	GZMM			JUNB
12	TNFRSF4				PHLDA1	HLA-DPB1			KLF2
13	TOX2				PTMS	ITM2C			MT1E
14	TSHZ2				RBPJ	KLRG1			MT1X
15					TNFRSF9	TRGC2

1.3 Subtype cluster markers

# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>% 
#   filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc_markers_02.tsv"))

## Hypergeometric test --------------------------------------

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v6 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v6_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
  
cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_"))

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster, -resolution) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)

rank	CD4.T.naive	CD4.T.CXCL13	CD4.T.reg	CD4.Th17	CD8.T.cytotoxic	CD8.T.CXCL13	CD8.T.ISG	NK.CD56	NK.cytotoxic	Cycling.T.NK	Cycling.T.NK_1	dissociated	dissociated_1	dissociated_2	doublet.Fibroblast	doublet.Fibroblast_1	doublet.Monocyte	doublet.Plasma.cell
1	IL7R	CXCL13	TNFRSF4	KLRB1	GZMK	CXCL13	IFIT3	GNLY	FGFBP2	STMN1	CENPF	CCL4L2	HSPA1A	KLF2	DCN	IGFBP5	HLA-DRA	SOX4
2	CCR7	NMB	IL2RA	IL4I1	CD8A	GZMB	ISG15	TYROBP	FCGR3A	TUBA1B	ASPM	CCL4	HSPA1B	CCR7	IGFBP5	TAGLN	CST3	PTCRA
3	KLF2	NR3C1	FOXP3	IL7R	CD8B	CCL4L2	MX1	AREG	SPON2	MKI67	MKI67	IFNG	MT1X	JUNB	RBP1	ADIRF	CXCL8	MAL
4	EEF1B2	MAF	CTLA4	LTB	ITM2C	MIR155HG	IFIT1	KLRC1	PRF1	HIST1H4C	HMGB2	FOS	DNAJB1	FOS	C7	DCN	SPP1	MZB1
5	TPT1	FKBP5	LTB	LST1	GZMH	TNFRSF9	RSAD2	FCER1G	KLRF1	TUBB	TOP2A	FOSB	MT1E	DUSP1	MEG3	IGFBP7	S100A9	DNTT
6	EEF1A1	IL6ST	RTKN2	TNFSF13B	CCL5	HAVCR2	IFIT2	TRDC	GNLY	TOP2A	UBE2C	TNF	HSPA6	SELL	CALD1	SPARCL1	S100A8	TFDP2
7	MAL	ITM2A	BATF	CCR6	TRGC2	RBPJ	IFI6	XCL1	KLRD1	TYMS	CCNB1	JUN	HSP90AA1	IL7R	IGFBP4	CALD1	HLA-DQA1	CD1E
8	TCF7	TSHZ2	TNFRSF18	CTSH	KLRG1	LAG3	MX2	KLRD1	NKG7	CENPF	UBE2S	EGR1	HSPH1	AREG	RARRES2	MGP	CD74	STMN1
9	CD40LG	CTLA4	SAT1	AQP3	GZMA	IFNG	IFI44L	KRT81	CX3CR1	HMGB2	PTTG1	CCL3L1	HSPE1	EEF1A1	NR2F2	C11orf96	LYZ	ARPP21
10	SELL	CD40LG	TBC1D4	CCL20	CCL4	CCL3	ISG20	XCL2	GZMB	ASPM	TPX2	NFKBID	HSPD1	CD69	SELENOP	MYL9	FTL	AC084033.3
11	GPR183	PDCD1	TIGIT	NFKBIA	CRTAM	PTMS	HERC5	IGFBP2	PLAC8	NUSAP1	STMN1	CD69	HSPB1	BTG2	MDK	TIMP3	APOE	CDK6
12	LDHB	CD4	GADD45A	RORA	CST7	CD8A	SAMD9L	CLIC3	CLIC3	PCLAF	KPNA2	NR4A2	HSP90AB1	GPR183	SOX4	MDK	HLA-DQB1	MAP1A
13	NOSIP	LIMS1	TNFRSF1B	TNFRSF25	GZMM	CRTAM	OAS1	IL2RB	PLEK	HMGN2	CENPE	AC020916.1	DNAJA1	PIK3IP1	ADIRF	TPM2	MARCKS	AC011893.1
14	SNHG8	TNFRSF4	PMAIP1	CEBPD	HLA-DPB1	PHLDA1	TNFSF10	CEBPD	TYROBP	H2AFZ	CKS2	EGR2	JUN	CD55	MGP	NR2F2	AIF1	GLUL
15	PABPC1	RNF19A	UGP2	TNFAIP3	DTHD1	FABP5	STAT1	KRT86	PTGDS	PCNA	TUBA1B	DUSP1	CACYBP	DNAJB1	EGR1	FBLN1	C1QB	ADA
16	NOP53	CORO1B	IKZF2	NCR3	PPP1R14B	TIGIT	EIF2AK2	TXK	EFHD2	HIST1H1B	HMMR	TNFSF9	HSPA8	FKBP5	STAR	IGF1	FTH1	AL357060.1
17	LEF1	RBPJ	TNFRSF9	SLC4A10	CD3G	KRT86	OAS3	CTSW	FCER1G	DUT	DLGAP5	KLF6	FKBP4	TSC22D3	SFRP4	RAMP1	BASP1	GRASP
18	EIF3E	CPM	ICOS	TMIGD2	THEMIS	JAML	SAMD9	KLRB1	CST7	CLSPN	CCNB2	TNFAIP3	CHORDC1	RACK1	CLU	SELENOP	C1QA	CD1B
19	LTB	ZBED2	LINC01943	DPP4	DUSP2	CCL5	XAF1	MATK	GZMH	SMC4	TUBB4B	DUSP2	RGS2	LDHB	ADAMTS1	RARRES2	C15orf48	MIR181A1HG
20	RACK1	AC004585.1	IL32	TPT1	CD3D	LINC01871	GBP1	CCL3	ADGRG1	ATAD2	NUSAP1	BTG2	FOS	SARAF	TIMP2	LUM	FN1	CCDC26
21	NACA	TOX2	SOX4	MYBL1	LYAR	CXCR6	EPSTI1	CD7	CCL3	SMC2	BIRC5	IER2	ANXA1	CXCR4	TCEAL4	IGFBP6	MNDA	JCHAIN
22	UBA52	DUSP4	ARID5B	S100A4	TC2N	TNIP3	IFI44	NKG7	HOPX	TPX2	HMGN2	PPP1R15A	DNAJB4	PLAC8	C1R	CARMN	G0S2	VIPR2
23	TOMM7	AHI1	CD27	CD40LG	EOMES	PDCD1	PLSCR1	CD63	IGFBP7	TMPO	ARL6IP1	NR4A1	PPP1R15A	EEF1B2	WFDC2	IGFBP4	APOC1	ID1
24	SOCS3	ICA1	BIRC3	SPOCK2	CXCR6	HLA-DRB1	IFI35	HOPX	ZEB2	HELLS	CDC20	NFKBIA	ZFAND2A	TCF7	FHL2	DST	SOD2	SOCS2
25	JUNB	ARID5B	LAYN	LINC01871	HLA-DPA1	GAPDH	USP18	TMIGD2	PRSS23	UBE2C	CDKN3	JUND	FOSB	TPT1	IFITM3	CAV1	NPC2	RCAN1
26	SERINC5	CD84	CORO1B	ERN1	HLA-DRB1	FAM3C	OASL	CMC1	AKR1C3	NASP	SMC4	GADD45B	DNAJA4	FOSB	PEG3	ID4	LST1	CLDN5
27	TMEM123	CCDC50	TYMP	JAML	SLF1	CTLA4	MT2A	TNFRSF18	CD247	DEK	CKS1B	TSC22D3	DUSP1	NOSIP	C1S	NUPR1	GSN	CASC15
28	EEF2	IGFL2	DUSP4	FKBP11	APOBEC3G	SPRY1	HELZ2	KLRC2	MYBL1	RRM2	KIF20B	TAGAP	TSPYL2	SC5D	LUM	CSRP2	MS4A6A	PFKFB2
29	FXYD5	RGS1	CD4	IFNGR1	PPP2R5C	CCND2	TRIM22	SRGAP3	AREG	CKS1B	GTSE1	ZFP36	SERPINH1	NACA	SERPINF1	CDKN1C	IL1B	GALNT2
30	TSHZ2	BATF	ENTPD1	MGAT4A	KIAA1551	CD63	CMPK2	GSTP1	C1orf21	KNL1	TUBA1C	ZFP36L1	UBC	AP3M2	CEBPD	PGR	PSAP	HES4
31	TRABD2A	SRGN	CTSC	S100A6	F2R	CD8B	LY6E	LAT2	S1PR5	HMGB1	TUBB	RGCC	UBB	BTG1	AKAP12	COL6A1	GRN	MARCKSL1
32	ANK3	CH25H	MIR4435-2HG	ELK3	SLAMF7	GZMH	PARP14	GZMB	CTSW	MCM7	SGO2	IL7R	AHSA1	NOP53	TIMP1	COL6A2	CD83	TP53INP1
33	SARAF	SPOCK2	LINC02099	EEF1A1	CXCR4	ENTPD1	NT5C3A	LINC00996	ABHD17A	FABP5	H2AFZ	CRTAM	NEU1	ZBTB16	CST3	EMX2	CTSH	APBA2
34	AQP3	ZNRF1	MAGEH1	KIT	SH2D1A	SRGAP3	DDX58	PRF1	KLF2	UBE2S	JPT1	KDM6B	DNAJB6	ERAP2	NR2F1	PALLD	GLUL	TSHR
35	RIPOR2	CHN1	SPOCK2	LTC4S	FAM102A	VCAM1	IRF7	KLRF1	PTPN12	GAPDH	CEP55	NR4A3	GADD45B	CCND3	DLK1	PPP1R14A	MEF2C	NREP
36	AP3M2	TNFRSF25	PHACTR2	RUNX2	CD52	ID2	RNF213	NCAM1	TTC38	CXCL13	NUF2	ATF3	KLF6	EEF1D	SERPING1	RBP1	CYBB	MME
37	ZFAS1	CD200	CARD16	RORC	YBX3	HLA-DRA	DDX60L	CXXC5	KLRB1	RANBP1	KIF14	DDX3X	BTG2	PPP1R15A	BEX3	C7	CTSB	AC002454.1
38	LINC02273	METTL8	S100A4	ZBTB16	STK17A	ITGAE	DDX60	SH2D1B	CCL4	H2AFX	KNL1	DUSP6	JUNB	PLK3	C11orf96	MFGE8	CSF3R	CHI3L2
39	EIF4B	RILPL2	STAM	FAM241A	CCR5	DUSP4	SAT1	MCTP2	PTGDR	MCM3	CENPA	GZMK	TNF	ZFP36L2	FILIP1L	KANK2	CD14	SMIM3
40	TOB1	TNFRSF18	GLRX	PDE4D	GPR174	LYST	PPM1K	IFITM3	ITGB2	EZH2	CDK1	KLRG1	ERN1	EEF2	RARRES1	NR2F1	C1QC	SSBP2
41	SESN3	SLA	SPATS2L	IL23R	COTL1	TNFSF4	PNPT1	ZNF683	XBP1	TUBB4B	HMGB3	JUNB	JUND	HNRNPA1	COL1A2	MFAP4	SGK1	UHRF1
42	NSA2	SMCO4	AC005224.3	B3GALT2	CCL4L2	NDFIP2	PARP9	ITGA1	CEP78	H2AFV	CDCA8	RASGEF1B	ATF3	VSIR	NUPR1	COL1A1	SPI1	LRRC28
43	PASK	BTLA	MAF	CERK	CD3E	AKAP5	IFIH1	IFITM2	ARL4C	CDK1	PLK1	ANXA1	DEDD2	PASK	TCEAL9	PBX1	FCGRT	BCL11A
44	TNFRSF25	NAP1L4	PBXIP1	TLE1	TUBA4A	GOLIM4	SP110	CCL5	BIN2	HIST1H1D	AURKA	MCL1	CD69	EIF3H	MARCKSL1	GSN	EGR1	SCAI
45	FAU	FYB1	F5	EEF1B2	PECAM1	CD27	OAS2	ITGAX	LITAF	HNRNPAB	TROAP	CXCR4	CLK1	TXNIP	SLC40A1	PDGFRB	FCGR2A	ATP6AP1L
46	EEF1D	MIR155HG	SLAMF1	CFH	ARAP2	SNAP47	C19orf66	CD38	TRDC	CENPE	H2AFV	IER5	IER5L	LDLRAP1	CFH	SERPINF1	PLAUR	RUFY3
47	LDLRAP1	PTPN13	BTG3	PERP	ITGA1	RGS1	STAT2	SAMD3	TXK	TK1	KIF2C	MYADM	H3F3B	CMTM8	APOE	SFRP1	CPVL	CD79A
48	CTSL	SESN3	TRAC	PLAT	JAML	HLA-DPA1	LAG3	SLC16A3	MYOM2	ZWINT	MAD2L1	CD8A	NR4A1	SCML1	GNG11	LHFPL6	MS4A7	GNA15
49	ITGA6	BIRC3	IL1R1	KIF5C	CD84	LINC02446	LAP3	CAPN12	GZMM	BIRC5	NUCKS1	PTGER4	CXCR4	LINC00402	CDKN1C	PLAC9	ALDH2	HHIP-AS1
50	PFDN5	TP53INP1	DNPH1	PLCB1	AOAH	SAMSN1	APOL6	CD247	CD300A	PTTG1	RAD21	ZFP36L2	EIF4A2	RIPK2	NBL1	SERPING1	SERPINA1	GSTM3

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]

cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes),
         )
  
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))

4 sub clustering

big_clusters <- list(
  CD4.T.CXCL13 = "CD4.T.CXCL13", 
  CD4.T.naive = "CD4.T.naive", 
  CD4.T.reg = "CD4.T.reg", 
  CD4.Th17 = "CD4.Th17", 
  CD8.T.CXCL13 = "CD8.T.CXCL13", 
  CD8.T.cytotoxic = "CD8.T.cytotoxic", 
  CD8.T.ISG = "CD8.T.ISG", 
  Cycling.T.NK = "Cycling.T.NK", 
  NK.CD56 = "NK.CD56", 
  NK.cytotoxic = "NK.cytotoxic"
)

cells_to_keep_list <- lapply(big_clusters, function(x) 
colnames(seu_obj_sub)[seu_obj_sub$cluster_label %in% x])

# seu_list <- lapply(cells_to_keep_list, function(x) subset(seu_obj_sub, cells = x))
# 
# preprocess_wrapper <- . %>%
#   FindNeighbors(reduction = "harmony", dims = 1:50) %>%
#   FindClusters(res = 0.1) %>%
#   FindClusters(res = 0.2) %>%
#   FindClusters(res = 0.3)
# 
# seu_list <- lapply(seu_list, preprocess_wrapper)
# write_rds(seu_list, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub_clusters.rds"))

seu_list <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub_clusters.rds"))

get_cluster_vector <- function(cluster_label, cluster_res = RNA_snn_res.0.3) {
  cluster_res <- enquo(cluster_res)
  cbind(cell_id = colnames(seu_list[[cluster_label]]), FetchData(seu_list[[cluster_label]], c(as_label(cluster_res)))) %>%
    as_tibble %>%
    mutate(cluster_extended = paste0(cluster_label, "_", !!cluster_res)) %>%
    select(cell_id, cluster_extended) %>% 
    deframe
}

cluster_vector_list <- lapply(big_clusters, get_cluster_vector)

cluster_vector <- unlist(cluster_vector_list, use.names = F) %>% setNames(lapply(cluster_vector_list, names) %>% unlist(use.names = F))

seu_obj$cluster_extended <- cluster_vector[seu_obj$cell_id] %>%
  setNames(seu_obj$cell_id)

seu_obj_sub$cluster_extended <- cluster_vector[seu_obj_sub$cell_id] %>%
  setNames(seu_obj_sub$cell_id)

cluster_extended_uniq <- as.character(na.omit(unique(seu_obj_sub$cluster_extended)))

Idents(seu_obj_sub) <- seu_obj_sub$cluster_extended
Idents(seu_obj) <- seu_obj$cluster_extended

# marker_tbl_extended <- lapply(
#   cluster_extended_uniq,
#   function(x) {
#     marker_tbl <- FindMarkers(seu_obj_sub, ident.1 = x) %>%
#       as_tibble(rownames = "gene") %>%
#       separate(gene, into = c("gene", "drop"), sep = "\\.\\.\\.") %>%
#       select(-drop) %>% 
#       mutate(cluster_extended = x)
#     write_tsv(marker_tbl, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_subcluster_", x, ".tsv"))
#     return(marker_tbl)
# }) %>% bind_rows
#  
# write_tsv(marker_tbl_extended, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_extended.tsv"))

marker_tbl_extended <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_extended.tsv"))
 
cluster_label_extended <- c(
  CD4.T.naive_0 = "CD4.T.naive.1", 
  CD4.T.naive_1 = "CD4.T.naive.2", 
  CD4.T.CXCL13_0 = "CD4.T.CXCL13.early", 
  CD4.T.CXCL13_1 = "CD4.T.CXCL13.late.1", 
  CD4.T.CXCL13_2 = "CD4.T.CXCL13.late.2", 
  CD4.T.reg_0 = "CD4.T.reg.2", 
  CD4.T.reg_1 = "CD4.T.reg.1", 
  CD4.T.reg_2 = "CD4.T.reg.ISG", 
  CD4.T.reg_3 = "CD4.T.reg.3", 
  CD4.Th17_0 = "CD4.Th17.1", 
  CD4.Th17_1 = "CD4.Th17.2", 
  CD8.T.cytotoxic_0 = "CD8.T.naive.2", 
  CD8.T.cytotoxic_1 = "CD8.T.cytotoxic", 
  CD8.T.cytotoxic_2 = "dissociated_3", 
  CD8.T.cytotoxic_3 = "CD8.T.naive.1", 
  CD8.T.cytotoxic_4 = "gd.T.cell", 
  CD8.T.cytotoxic_5 = "doublet.Plasma.cell_1", 
  CD8.T.CXCL13_0 = "CD8.T.CXCL13.early", 
  CD8.T.CXCL13_1 = "CD8.T.CXCL13.late", 
  CD8.T.CXCL13_2 = "NK.reg.CRTAM", 
  CD8.T.ISG_0 = "CD8.T.ISG", 
  CD8.T.ISG_1 = "CD4.T.ISG", 
  CD8.T.ISG_2 = "dissociated_4", 
  NK.CD56_0 = "NK.reg.IGFBP2",
  NK.CD56_1 = "NK.reg.CD56", 
  NK.CD56_2 = "NK.reg.ISG", 
  NK.CD56_3 = "NK.reg.KRT81.KRT86", 
  NK.CD56_4 = "NK.reg.CCL3",
  NK.cytotoxic_0 = "NK.cytotoxic.SPON2.2", 
  NK.cytotoxic_1 = "NK.cytotoxic.GZMH", 
  NK.cytotoxic_2 = "NK.cytotoxic.SPON2.1",
  Cycling.T.NK_0 = "Cycling.CD8.T.1",
  Cycling.T.NK_1 = "Cycling.CD8.T.2", 
  Cycling.T.NK_2 = "Cycling.CD8.T.3", 
  Cycling.T.NK_3 = "Cycling.CD8.T.4", 
  Cycling.T.NK_4 = "Cycling.NK.3", 
  Cycling.T.NK_5 = "Cycling.CD4.T", 
  Cycling.T.NK_6 = "Cycling.NK.2", 
  Cycling.T.NK_7 = "Cycling.NK.1"
)

names(seu_obj$cluster_label) <- colnames(seu_obj)
seu_obj$cluster_label[is.na(seu_obj$cluster_label)] <- "NA"
Idents(seu_obj) <- seu_obj$cluster_label
seu_obj$cluster_label_sub <- cluster_label_extended[seu_obj$cluster_extended]
names(seu_obj$cluster_label_sub) <- colnames(seu_obj)
seu_obj$cluster_label_sub[is.na(seu_obj$cluster_label_sub)] <- "NA"
Idents(seu_obj) <- seu_obj$cluster_label_sub

names(seu_obj_sub$cluster_label) <- colnames(seu_obj_sub)
seu_obj_sub$cluster_label[is.na(seu_obj_sub$cluster_label)] <- "NA"
Idents(seu_obj_sub) <- seu_obj_sub$cluster_label
seu_obj_sub$cluster_label_sub <- cluster_label_extended[seu_obj_sub$cluster_extended]
names(seu_obj_sub$cluster_label_sub) <- colnames(seu_obj_sub)
seu_obj_sub$cluster_label_sub[is.na(seu_obj_sub$cluster_label_sub)] <- "NA"
Idents(seu_obj_sub) <- seu_obj_sub$cluster_label_sub


marker_sheet_extended <- marker_tbl_extended %>%
  mutate(cluster_label_sub = cluster_label_extended[cluster_extended]) %>% 
  group_by(cluster_label_sub) %>%
  mutate(rank = row_number()) %>% 
  slice(1:50) %>% 
  select(rank, gene, cluster_label_sub) %>% 
  arrange(cluster_label_sub) %>% 
  spread(cluster_label_sub, gene)

# marker_sheet_joined <- marker_sheet %>% 
#   select(-CD8.T.ISG) %>% 
#   left_join(marker_sheet_extended, by = "rank") %>%
#   gather(cluster_label, gene, -rank) %>% 
#   mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
#   spread(cluster_label, gene)

cluster_n_tbl_full <- seu_obj$cluster_label_sub %>% 
  table() %>% 
  enframe("cluster_label_sub", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n)) %>% 
  filter(cluster_label_sub != "NA")

marker_tbl_extended_annotated <- marker_tbl_extended %>% 
  mutate(cluster_label_sub = cluster_label_extended[cluster_extended]) %>%
  select(-cluster_extended) %>% 
  left_join(cluster_n_tbl_full, by = "cluster_label_sub")

# marker_tbl_annotated_full <- marker_tbl_annotated %>% 
#   filter(!(cluster_label %in% unlist(big_clusters))) %>% 
#   bind_rows(marker_tbl_extended_annotated) %>% 
#   mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
#   arrange(cluster_label, -avg_logFC)


cells_to_keep <- colnames(seu_obj_sub)[!(str_detect(seu_obj_sub$cluster_label_sub, "dissociated|doublet"))]
# seu_obj_sub_sub <- subset(seu_obj_sub, cells = cells_to_keep)
# seu_obj_sub_sub <- RunUMAP(seu_obj_sub_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# write_rds(seu_obj_sub_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub.rds"))

seu_obj_sub_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_sub.rds"))

formattable::formattable(marker_sheet_extended)

rank	CD4.T.CXCL13.early	CD4.T.CXCL13.late.1	CD4.T.CXCL13.late.2	CD4.T.ISG	CD4.T.naive.1	CD4.T.naive.2	CD4.T.reg.1	CD4.T.reg.2	CD4.T.reg.3	CD4.T.reg.ISG	CD4.Th17.1	CD4.Th17.2	CD8.T.CXCL13.early	CD8.T.CXCL13.late	CD8.T.cytotoxic	CD8.T.ISG	CD8.T.naive.1	CD8.T.naive.2	Cycling.CD4.T	Cycling.CD8.T.1	Cycling.CD8.T.2	Cycling.CD8.T.3	Cycling.CD8.T.4	Cycling.NK.1	Cycling.NK.2	Cycling.NK.3	dissociated_3	dissociated_4	doublet.Plasma.cell_1	gd.T.cell	NK.cytotoxic.GZMH	NK.cytotoxic.SPON2.1	NK.cytotoxic.SPON2.2	NK.reg.CCL3	NK.reg.CD56	NK.reg.CRTAM	NK.reg.IGFBP2	NK.reg.ISG	NK.reg.KRT81.KRT86
1	CXCL13	CXCL13	CXCL13	IFIT3	CCR7	IL7R	RTKN2	TNFRSF4	TNFRSF4	ISG15	KLRB1	LST1	CXCL13	CXCL13	GZMH	IFIT3	CD8B	GZMK	STMN1	STMN1	STMN1	CXCL13	CXCL13	SPON2	CENPF	CENPF	GZMK	XIST	IGHG3	TRGC2	FGFBP2	SPON2	FGFBP2	CCL3	GNLY	XCL2	TYROBP	ISG15	GNLY
2	MAF	NMB	PDCD1	ISG15	RPS8	CD40LG	FOXP3	IL2RA	TNFRSF18	SAT1	IL7R	IL4I1	CCL4L2	KRT86	CD8A	ISG15	IL7R	CD8A	MKI67	TUBA1B	MKI67	STMN1	HLA-DRA	FCGR3A	ASPM	MKI67	MALAT1	MALAT1	IGHG1	XCL1	GZMH	FGFBP2	FCGR3A	AREG	AREG	GZMB	KLRC1	GNLY	KLRC1
3	IL6ST	CPM	CTLA4	MX2	RPS6	ANXA1	LTB	FOXP3	TNFRSF9	HERC5	IL4I1	TNFSF13B	HAVCR2	TNFRSF9	CD8B	IFIT1	CD8A	CD8B	TUBA1B	HIST1H4C	TOP2A	TYMS	HLA-DRB1	IGFBP7	MKI67	STMN1	PTPRC	NEAT1	IGLC3	ZNF683	CX3CR1	FCGR3A	SPON2	CCL4L2	KLRC1	CRTAM	FCER1G	IFIT3	KRT81
4	NR3C1	NR3C1	MAF	IFIT2	RPS3A	GPR183	UGP2	BATF	IL2RA	MX1	LTB	PCDH9	MIR155HG	GZMB	CCL5	MX1	YBX3	KLRG1	HMGB2	MKI67	ASPM	TUBA1B	GZMB	KLRF1	TOP2A	TOP2A	EDF1	MT-ND3	IGLC2	KLRC2	NKG7	CCL3	KLRF1	XCL2	ITGAX	XCL1	TRDC	FCER1G	FCER1G
5	FKBP5	IGFL2	ZBED2	IFI44L	SELL	KLF2	TBC1D4	TNFRSF18	BATF	TNFRSF4	CCR6	SPINK2	PTMS	RBPJ	HLA-DRB1	IFIT2	ANXA1	ITM2C	TOP2A	TUBB	TYMS	PCNA	IFNG	CX3CR1	KNL1	ASPM	RPS29	MT-ND1	IGHG4	LINC02446	TRGC2	PRF1	PRF1	XCL1	FCER1G	FABP5	GNLY	MX1	AREG
6	CD40LG	FKBP5	RBPJ	IFIT1	EEF1B2	S100A4	IL2RA	LTB	CTLA4	SPATS2L	TNFSF13B	KIT	CD8A	SRGAP3	GZMA	RSAD2	RPS12	CCL4	TUBB	TYMS	HELLS	PCLAF	FABP5	AKR1C3	NUSAP1	TUBA1B	RPL36AL	MT-CO3	IGKC	HOPX	KLF2	KLRF1	GNLY	FOS	XCL1	TNFRSF9	AREG	IFIT1	TNFRSF18
7	CD4	ITM2A	DUSP4	RSAD2	RPS12	FOS	PMAIP1	CTLA4	GADD45A	IFI6	LST1	SCN1B	LAG3	FAM3C	GZMK	IFI6	TOB1	DUSP2	CENPF	TOP2A	CLSPN	DUT	COTL1	GINS2	CENPE	HMGB2	RPL13A	MT-ND2	JCHAIN	XCL2	KLRG1	PTGDS	KLRD1	IER2	TYROBP	HSP90AB1	IGFBP2	TYROBP	KRT86
8	AC004585.1	ICA1	CD4	MX1	RPL34	LTB	IKZF2	GADD45A	TNFRSF1B	IL2RA	AQP3	LTC4S	GZMB	TIGIT	HLA-DPB1	ISG20	FOS	PPP1R14B	HMGN2	HMGB2	PCNA	FABP5	GAPDH	FGFBP2	TPX2	GNLY	ATP5MC2	MT-CYB	IGHGP	CTSW	PLEK	CX3CR1	NKG7	CCL4	TRDC	PKM	KRT81	IFI27	TRDC
9	CORO1B	IL6ST	CD40LG	IL7R	RPL32	TPT1	IL32	RTKN2	ICOS	CTLA4	CCL20	IL1R1	IFNG	SPRY1	PTMS	MX2	VIM	TRGC2	PTTG1	NUSAP1	UBE2C	CLSPN	NASP	MLC1	DLGAP5	TUBB	CALM1	MT-ATP6	IGHG2	CD63	PRF1	GZMB	CLIC3	EGR1	XCL2	GAPDH	KLRD1	IFITM3	TYROBP
10	ITM2A	TSHZ2	MIR155HG	IFI6	KLF2	NFKBIA	CTLA4	SOX4	PIM3	OAS1	CTSH	LINC00299	CCL3	IFNG	ZNF683	SAMD9L	JUNB	CST7	IL2RA	CENPF	PCLAF	HELLS	LAG3	S1PR5	GTSE1	NUSAP1	SRP14	MT-CO1	CD8A	CD7	ZEB2	IGFBP7	TYROBP	NFKBIA	KLRD1	NME1	CLIC3	KLRC1	XCL1
11	TSHZ2	TNFRSF4	PRDM1	TNFSF10	RPL9	TIMP1	TIGIT	TNFRSF1B	FOXP3	IFIT3	RORA	GSN	CXCR6	MIR155HG	HLA-DPA1	IFI44L	GZMK	DTHD1	PCLAF	PCLAF	GTSE1	NASP	MCM5	TYMS	KIF14	UBE2C	COMMD6	PTPRC	MZB1	TRGC1	PRSS23	PLAC8	GZMB	CCL3L1	CMC1	MIR155HG	KLRB1	AREG	CTSW
12	PDCD1	AHI1	TNFRSF18	STAT1	IL7R	S100A10	ARID5B	TIGIT	IL1R2	IFIT1	TNFRSF25	PLAT	HLA-DRB1	CD8A	CXCR6	HERC5	CCR7	CCL4L2	NUSAP1	HIST1H1B	ORC6	MCM3	PTMS	E2F1	HJURP	IGFBP2	RPS25	MT-CO2	CCL5	NCR3	KLRD1	GNLY	PLAC8	DUSP2	CTSW	SRM	CEBPD	IFI44L	ZNF683
13	TOX2	AC004585.1	ITM2A	HERC5	RPS13	JUN	SAT1	TBC1D4	LTB	FOXP3	SLC4A10	CD300LF	CCL5	LAG3	HLA-DRA	OAS1	ZFP36L2	CRTAM	ASPM	HMGN2	CDT1	MKI67	MIR155HG	MCM7	POLQ	TPX2	PFDN5	H3F3B	IGHA1	CD52	FCGR3A	FCER1G	CX3CR1	BTG2	NCAM1	NPW	KRT86	RSAD2	XCL2
14	CH25H	LIMS1	RGS1	ISG20	RPL21	S100A11	BATF	LINC01943	TBC1D4	TYMP	DPP4	AFF3	RBPJ	PHLDA1	ITM2C	MT2A	S100A6	GZMM	H2AFZ	ASPM	BRCA1	MCM5	HAVCR2	MYOM2	BUB1B	KLRC1	RPS15A	CCNI	CCL4	TRDC	MYBL1	NKG7	FCER1G	NR4A1	HOPX	KDM6B	TXK	CD38	HOPX
15	PTPN13	BIRC3	LINC01943	OAS1	TCF7	VIM	CORO1B	SAT1	PKM	LTB	MYBL1	DLL1	GZMH	ENTPD1	COTL1	STAT1	MT-ND1	CMC1	TYMS	H2AFZ	ZWINT	MCM7	CD8A	MCM4	HMMR	HMGN2	HINT1	GMFG	GZMK	KLRD1	ADGRG1	AKR1C3	PTGDS	FOSB	LINC00996	TSPAN17	XCL1	IFIT2	RGS16
16	CTSW	PGM2L1	TNFRSF4	OAS3	MAL	CTSL	MIR4435-2HG	TNFRSF9	BIRC3	RTKN2	S100A4	LIF	HLA-DRA	HAVCR2	HLA-DQA1	TNFSF10	KLF2	LYAR	CEP55	RRM2	ATAD2	MCM4	HELLS	CHEK1	KIF11	SMC4	RPS27	COX5B	CD8B	KIR3DL2	EFHD2	AREG	PLEK	CD69	KLRF1	ZBED2	IL2RB	IFI6	CD7
17	CCL5	CD200	ADAM19	EIF2AK2	RPL39	RPL34	TNFRSF4	UGP2	REL	EPSTI1	CD40LG	IL23R	PHLDA1	CCL3	IFNG	OASL	LYAR	CCL5	DUT	SMC4	RAD51AP1	TUBB	MCM6	CLSPN	CIT	FCER1G	FTH1	CALM2	RGS1	TRG-AS1	FLNA	CCL4	EFHD2	TYROBP	IFITM3	GOLIM4	MATK	TRDC	LGALS1
18	NKG7	SESN3	CCDC50	IFI44	RPL22	EEF1A1	AC005224.3	IKZF2	ENTPD1	STAT1	TPT1	RORC	PDCD1	CTLA4	CCL4	PLSCR1	CXCR4	EOMES	PCNA	PCNA	NUSAP1	GAPDH	ACTB	STMN1	SGO2	TYMS	HSPA8	PSMB1	GZMA	CCL5	GNLY	KLRD1	CST7	NR4A2	TXK	PAICS	NKG7	TNFSF10	CD63
19	KLRD1	RNF19A	TSHZ2	GBP1	RPL7	RPLP0	PBXIP1	PMAIP1	TYMP	ICOS	RPLP1	TGM2	HLA-DPA1	JAML	APOBEC3G	GBP1	NELL2	SH2D1A	TNFRSF4	DUT	DLGAP5	GINS2	MCM4	CHST2	C21orf58	GSTP1	DBI	TPM3	HLA-DPA1	TMSB4X	CST7	PLEK	CCL3	GADD45B	IL2RB	MRTO4	XCL2	ISG20	KLRC2
20	ST8SIA1	CHN1	CD2	CCR7	RPL5	RPS18	STAM	IL32	SAT1	ISG20	EEF1A1	TNFSF11	CD8B	CCND2	SH3BGRL3	SAMD9	S100A10	YBX3	TPX2	ATAD2	CDK1	HMGB2	STMN1	CTBP2	KIF23	KRT81	FAU	SOD1	CXCR6	LINC01871	HOPX	CLIC3	IGFBP7	KLRD1	KLRC2	GEM	CD63	GZMB	TMIGD2
21	LIMS1	GNG4	ETV7	EPSTI1	RPS18	RPS12	GADD45A	ICOS	IL1R1	MX2	B3GALT2	ZG16B	HLA-DPB1	PTMS	CD52	EIF2AK2	MT-ND2	GZMA	FOXP3	CLSPN	BIRC5	RANBP1	PCNA	FCER1G	AC007240.1	TRDC	PARK7	ATP5MPL	IKZF3	GNLY	C12orf75	EFHD2	ADGRG1	TRDC	MATK	EGR2	TMIGD2	SAMD9L	SRGAP3
22	SMCO4	MAF	MYO7A	MT2A	RPL37	RPL39	BTG3	LAYN	SDC4	TIGIT	RORC	ENPP1	VCAM1	LINC01871	CD3D	LAG3	TNFRSF18	TC2N	ENO1	UBE2C	TUBA1B	HLA-DRA	CLSPN	PLAC8	KIF15	H2AFZ	RPLP2	AES	LTB	ZFP36L2	GZMB	MYOM2	HOPX	KLRC1	SELL	CD72	CMC1	BST2	MATK
23	CD8A	BATF	TOX2	SAMD9L	RPL13	JUNB	BIRC3	LINC02099	LINC01943	RSAD2	LTK	PTGER3	LINC01871	GOLIM4	LAG3	IFI35	LMNA	CXCR4	BATF	HMGB1	NASP	MCM6	GINS2	RAMP1	NCAPG	TUBB4B	C9orf16	GNG5	APOC1	HCST	C1orf21	TYROBP	CD247	FCER1G	CD7	METTL1	CTSW	CLIC3	CXXC5
24	CTLA4	TOX2	CD200	XAF1	RPS5	TOB1	HPGD	CTSC	NAMPT	TNFRSF1B	CFH	IRAK3	JAML	GAPDH	C12orf75	EPSTI1	TSC22D3	PPP2R5C	CTLA4	HIST1H1D	MCM4	DEK	CDCA7	ORC6	BRCA2	AREG	RPL23A	SAP18	RPL22	IKZF2	LITAF	ADGRG1	AREG	NFKBID	GZMB	GFOD1	CD7	PLSCR1	GSTP1
25	CCL4	CH25H	BHLHE40-AS1	TMEM123	RPL3	AQP3	SELL	DUSP4	LTA	UGP2	NCR3	CA2	TNIP3	TNS3	CD74	OAS3	FOSB	A2M-AS1	GAPDH	TPX2	GINS2	H2AFZ	CHEK1	MCM2	ECT2	CENPE	RPL28	CIB1	PABPC1	FCRL6	PLAC8	CST7	ZEB2	ZFP36	SRGAP3	RGS16	CCL3	OAS1	IL2RB
26	FYB1	THADA	PTPN13	DDX58	EEF1A1	RPL11	ICOS	CD27	DNPH1	IFI44L	FAM241A	KIAA1211L	TNFSF4	LAYN	CD3G	XAF1	ZFP36	GZMH	HIST1H1B	H2AFX	CENPU	HMGN2	MCM2	TTC38	CKAP2L	UBE2S	COX7C	COPE	HLA-DPB1	IL32	S100A4	HOPX	AKR1C3	CEBPD	CEBPD	PARK7	LAT2	IFI35	LAT2
27	RUNX3	CD84	PDE7B	CMPK2	RPL11	TNFAIP3	AC133644.2	ARID5B	TIGIT	SAMD9	CEBPD	SLC4A10	CD63	NBL1	THEMIS	USP18	PPP1R14B	PDCD4	SMC4	SMC2	FEN1	SMC2	TYMS	GZMB	ARHGAP11B	CKS1B	CHCHD2	TRMT112	TPT1	NUCB2	SPON2	CD247	KLRB1	MAFF	CD63	NDFIP2	GSTP1	KLRD1	CSF1
28	RNF19A	BTLA	NKG7	CD40LG	RPS23	TNFRSF25	NABP1	CORO1B	TFRC	TNFRSF18	RPLP0	COL4A4	HLA-DQA1	MYO1E	LINC02446	LY6E	DUSP1	HLA-DPB1	SMC2	TMPO	TUBB	DNMT1	CDT1	PCNA	ANLN	TNFRSF18	RPL4	PSMB9	CD74	CD9	FCRL6	PRSS23	PTPN12	IGFBP2	NKG7	IFNG	HOPX	EPSTI1	SPRY1
29	PRF1	CORO1B	IFNG	SAMD9	RPL23	FXYD5	CD27	ENTPD1	F5	SAMD9L	RPS12	NRP1	ID2	RGS2	HLA-DQB1	CMPK2	CD55	F2R	BIRC5	CKS1B	CHEK1	TPI1	MALAT1	PLEK	ESPL1	HIST1H1B	RPS24	BRK1	GZMH	CD8A	ADRB2	ABHD17A	PRSS23	KDM6B	TNFRSF18	COL6A3	CCL5	IRF7	CAPG
30	RGS1	METTL8	NR3C1	LY6E	TPT1	RPL3	IKZF4	MAGEH1	PMAIP1	TBC1D4	ERN1	B3GALT5	FABP5	MYO7A	LGALS1	NT5C3A	CD69	SLAMF7	PKM	UBE2S	KNL1	ATAD2	HLA-DQA1	MCM5	NUF2	KPNA2	COX8A	RBX1	IGHM	KLRC4	ARL4C	CTSW	C1orf21	IER3	IFITM2	CXCL13	KLRF1	CEBPD	ITGA1
31	GZMB	CCDC50	LIMS1	GPR183	RPLP0	RPL9	PHTF2	S100A4	CCND2	BATF	KIF5C	PDZK1	LINC02446	AKAP5	HLA-DRB5	IFI44	GPR183	TNFSF9	RRM2	KNL1	SMC2	BRCA1	MYO7A	HES6	KIF4A	DLGAP5	RPS16	HSPA8	RPL8	MATK	S1PR5	ZEB2	TXK	CD160	CD300A	TNFRSF18	SH2D1B	XCL2	CLIC3
32	IL6R	SMCO4	RNF19A	USP18	RPL30	RPL32	PHACTR2	BIRC3	DUSP4	USP18	RPL13	EMID1	TNFRSF9	DGKH	S100A6	HELZ2	TYROBP	STK17A	PMAIP1	TUBB4B	MCM5	HNRNPAB	MKI67	PRF1	KIFC1	CKS2	RPL35A	S100A11	CD3E	CRTAM	KLF3	CHST2	S1PR5	REL	GSTP1	RGCC	PRF1	HERC5	LDLRAD4
33	CST7	CD4	RDH10	DDX60L	RPS28	RPS3A	RGS1	CD4	LINC02099	COX5A	SPOCK2	HOXA5	TIGIT	SNX9	FABP5	IRF7	FCER1G	PKM	TK1	HIST1H1C	KIF11	SLBP	HLA-DPA1	FEN1	BUB1	XCL2	RPL7	VAMP8	HLA-DQB1	S100A6	PATL2	PTPN12	CEBPD	GNLY	KRT81	CD82	ITGA1	NT5C3A	KIR2DL4
34	SLC9A9	ARMH1	GAPDH	TRIM22	LEF1	RPS6	DUSP16	MAF	CCL20	SOX4	JAML	LINGO4	ITGAE	DUSP4	ANXA5	CD38	CCL3	COX5A	JPT1	EZH2	TPX2	SNRPB	SH3BGRL3	CDT1	KIF20B	SMC2	RPS7	POMP	WNK1	CAPG	ANXA1	MYBL1	TRDC	KLRF1	SPTSSB	MALAT1	SRGAP3	IL2RB	IGFBP2
35	BTLA	PHACTR2	SNX9	HELZ2	RPS20	CD4	TRAC	SPOCK2	SH2D2A	IKZF2	TIGIT	NECTIN2	LYST	LINC01480	CCL4L2	TRIM22	CTSC	EIF2AK2	ANP32B	DEK	HIST1H1B	GZMB	MCM3	ADGRG1	FBXO43	TMPO	RPS11	EDF1	CD4	IFITM2	TGFBR3	TTC38	CTSW	CLIC3	IGFBP2	RBPJ	MCTP2	LY6E	TXK
36	TP53INP1	TNFRSF25	IL6ST	ANXA1	RPL38	RPL36	CARD16	IL1R1	ZBTB32	PMAIP1	RPL34	HOXA7	APOBEC3C	ITGAE	TMSB4X	DDX58	RGCC	ACP5	TMPO	CDK1	GMNN	COTL1	PTTG1	CCDC28B	NCAPG2	CDK1	ATP5MG	NDUFS5	HLA-DRA	MAF	CTSW	S1PR5	GZMH	TXK	CAPN12	RANBP1	MAPK1	XAF1	LINC01871
37	TNFSF8	ARID5B	CORO1B	NT5C3A	RPS16	RPL30	LINC01943	GLRX	ID3	CTSC	S100A6	PRAM1	CD27	RGS1	LGALS3	PARP14	AC020916.1	ADGRG1	CENPE	BIRC5	MCM7	DNAJC9	MCM7	GNLY	PRC1	CTSW	MYL12B	UQCR11	RPS13	CD4	S100A6	ITGB2	TTC38	MATK	CXXC5	EIF5A	CXXC5	XCL1	CD9
38	GZMH	CTLA4	ITGA2	PLSCR1	RPS4X	RPS8	S100A4	CARD16	SOX4	NT5C3A	RPL32	B4GALNT1	HLA-DMA	CD63	ITGA1	CD8A	CTLA4	IFI35	UBE2C	H2AFV	RRM1	HLA-DRB1	RBPJ	ASCL2	DIAPH3	KNL1	RPL39	LDHB	CD3G	GLUL	TTC16	XBP1	ABHD17A	KLRB1	IL18	DCTPP1	KLRC2	PRF1	SLC16A3
39	GNLY	ZBED2	SMCO4	IFIH1	RPL36	RPS27A	TNFRSF1B	TYMP	PTP4A3	TNFRSF9	HLA-DPB1	LPAR1	CRTAM	SNAP47	TRGC2	DDX60	CD4	CSF1	CLSPN	TK1	WDR76	CENPX	CD8B	PTPN12	SMC4	KRT86	UBB	HNRNPA1	RPL29	PRMT9	A2M-AS1	CD160	PTGDR	NKG7	ID2	ENO1	SAMD3	SAMD9	CCL5
40	METTL8	TNFRSF18	SAMSN1	IFI35	RPLP2	RPSA	MAGEH1	MIR4435-2HG	MIR155HG	IFI44	TLE1	SLCO2A1	NKG7	CLNK	APOBEC3C	PPM1K	DNAJB1	NDFIP2	CKS2	CENPM	RRM2	IFNG	PDCD1	PCLAF	RPL10	PCLAF	RPS21	ANP32B	TNFRSF18	SCML4	TTC38	LAIR2	MYBL1	EGR2	PTGDR	TPI1	LINC00996	MX2	NCR3
41	ADAM19	RILPL2	SRGN	IRF7	RPS27	RPL13	CD4	DNPH1	ARID5B	ARID5B	TMIGD2	CD33	CTLA4	TNIP3	SIT1	IFIH1	MYBL1	LGALS3	ACTB	ZWINT	CENPF	CHEK1	BRCA1	CDC45	RPS27	TYROBP	RPL13	ABRACL	LARP7	LYAR	ASCL2	KLRB1	CMC1	IL2RB	KLRB1	CAMK1	CD247	OASL	PRMT9
42	CD2	AGFG1	ICOS	DDX60	RPS2	EEF1B2	SPOCK2	PHACTR2	CD27	LINC01943	FKBP11	APOL4	RGS1	ADAMTS6	JAML	RNF213	CD38	TMEM123	CALM3	HIST1H1E	UBE2T	CCL3	ITGA2	GMNN	BRCA1	XCL1	ANP32B	TRAPPC1	DUSP23	CTSA	VCL	TXK	CHST2	MAP3K8	SH2D1B	TNIP3	PTGDR	USP18	CLNK
43	SPOCK2	CYSLTR1	KLRD1	OAS2	RPL12	RPL38	CCNG2	IL1R2	ENO1	GBP5	HLA-DRB1	RASSF8	ACP5	BCL2L11	LINC01871	DDX60L	TNFRSF4	IL6ST	ATAD2	FAM111B	CDCA7	CDT1	CD63	CENPU	RPL41	HMMR	COX6C	ATP5F1D	DUSP2	IFITM3	LILRB1	PTGDR	CCL4	IRF8	C1orf162	PIM3	NCAM1	EIF2AK2	CTSA
44	GZMA	CD40LG	CD109	PNPT1	SNHG8	RPLP1	FCMR	PHLDA1	IKZF2	OAS3	RPS27A	TLE1	SNAP47	NDFIP2	CD2	OAS2	TNFAIP3	CD247	ANP32E	KIF11	CDKN3	RAN	CCL5	C1orf21	RPL30	TMIGD2	RPS28	SNRPB	SELL	PTGDR	FGR	CD300A	SH2D1B	KRT81	PLAC8	NAMPT	STARD3NL	ERICH3	KLRB1
45	CD8B	BHLHE40-AS1	CD3D	PPM1K	RPS27A	RPL5	TLK1	STAM	CD4	CD4	KLRD1	AL034397.3	ITM2A	RAB27A	CST7	PARP9	HLA-DRA	TPI1	ICOS	FABP5	SGO2	PKM	CCL3	FGR	RPL13	CDKN3	RPL36	SEPT7	KLF2	SPRY1	PLEKHG3	FGR	CEP78	SRGN	KIR2DL4	TNFRSF1B	CAPN12	CCL3	CTSD
46	MATK	IGFBP4	SLAMF1	PARP9	RPL19	RPS16	SMAP2	SLAMF1	LAIR2	RNF213	RUNX2	STAC	SAMSN1	TNFRSF1B	CCR5	PNPT1	JUN	ANKRD28	CKS1B	DLGAP5	USP1	ENO1	HLA-DMA	LYN	RPS27A	H2AFX	LDHB	ZFAS1	CXCR4	CTLA4	COL6A2	GSAP	XBP1	LAT2	MCTP2	ZNF593	TNFRSF18	IFI44	NDFIP2
47	CD84	ELMO1	RPL23A	LGALS3BP	PABPC1	CD52	LAYN	LAIR2	SYNGR2	SPOCK2	RPL10	BCAS1	CCND2	ID2	CRTAM	GZMK	NCF1	CD226	PHF19	GTSE1	FANCI	SNRPD1	GZMH	KIR2DL3	RPS12	CD63	SUMO2	UQCRH	DNAJC7	DDAH2	CES1	CTBP2	SAMD3	ZBTB16	ZNF683	HSPA5	AOAH	CD7	ADGRG3
48	ARID5B	MS4A6A	AHI1	LTB	RPS14	LMNA	PIM2	F5	SERPINB9	ENTPD1	HLA-DPA1	CSF2	APOBEC3G	LYST	GRAP2	BST2	TRDC	TMEM173	TNFRSF18	TMEM106C	MT-ND4	NUDT1	APOBEC3G	ITGAM	PRR11	CCNB1	RPL14	SEC61G	TRBC2	GUK1	LINC02384	GK5	MATK	GRASP	MMP25-AS1	FAM3C	GZMA	OAS3	CD247
49	CD63	ST8SIA1	ARID5B	LAMP3	RPS21	RPL37	SAMHD1	TRAC	NR4A1	CD27	RPS13	TOX2	CD74	AHI1	IDH2	SP110	HAVCR2	TPM4	UBE2S	ANP32B	ATAD5	PGAM1	ZNF683	MCM10	RPS14	GTSE1	RPS19	PPP1CA	SERPINB6	CD28	COTL1	C1orf21	ITGB2	NR4A3	MAFF	SLC7A5	APOBEC3G	KLRC2	GZMB
50	HOPX	MAGEH1	TYMP	GZMA	RPL35A	RPS29	ICA1	PHTF2	VDR	LY6E	GZMB	ALDOC	DUSP4	ZEB2	GZMM	C19orf66	MYADM	USP18	RANBP1	HNRNPAB	CENPE	ATAD5	ANXA5	CD300A	TPT1	BIRC5	ATP5F1E	RHOA	TYROBP	CCR7	CD27	MTSS1	CD300A	EGR3	XBP1	PGAM1	METRNL	WARS	CKLF

write_tsv(marker_sheet_extended, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet_full.tsv"))

write_tsv(marker_sheet_extended, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet_full.tsv"))

write_tsv(marker_tbl_extended_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated_full.tsv"))

write_tsv(marker_tbl_extended_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated_full.tsv"))

plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub_sub, c(myfeatures, "cluster_label_sub"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cell_id = colnames(seu_obj_sub_sub),
         cluster_label_sub = ordered(cluster_label_sub, levels = names(clrs$cluster_label_sub[[coi]])),
         ) %>%
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
  
if (cell_sort == "CD45+") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label_sub)) + 
  common_layers_disc +
  # facet_wrap(~cluster_label_sub) +
  scale_color_manual(values = clrs$cluster_label_sub[[coi]]) +
  ggtitle("Sub cluster")

4.1 add module scores and pathway scores

# signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
#   gather(module, gene) %>%
#   na.omit() %>%
#   group_by(module) %>%
#   do(gene = c(.$gene)) %>%
#   {setNames(.$gene, .$module)}
# 
# signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
# 
# ## compute expression module scores
# for (i in 1:length(signature_modules)) {
#   seu_obj_sub_sub <- AddModuleScore(seu_obj_sub_sub, features = signature_modules[i], name = names(signature_modules)[i])
#   seu_obj_sub_sub[[names(signature_modules)[i]]] <- seu_obj_sub_sub[[paste0(names(signature_modules)[i], "1")]]
#   seu_obj_sub_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
#   print(paste(names(signature_modules)[i], "DONE"))
# }
# 
# ## compute progeny scores
# progeny_list <- seu_obj_sub_sub@assays$RNA@data[VariableFeatures(seu_obj_sub_sub),] %>%
#   as.matrix %>%
#   progeny %>%
#   as.data.frame %>%
#   as.list
# 
# names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
# 
# for (i in 1:length(progeny_list)) {
#   seu_obj_sub_sub <- AddMetaData(seu_obj_sub_sub,
#                                  metadata = progeny_list[[i]],
#                                  col.name = names(progeny_list)[i])
# }
# 
# write_rds(seu_obj_sub_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))

seu_obj_sub_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))

4.2 marker heatmap

marker_top_tbl <- marker_sheet_extended[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet|dissociated")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub_sub, c("cluster_label_sub", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cluster_label_sub = ordered(cluster_label_sub, levels = names(clrs$cluster_label_sub[[coi]]))) %>% 
  group_by(cluster_label_sub, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label_sub[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label_sub, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_wrap(~cluster_label_x, scales = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

4.3 composition

4.3.1 per site

comp_site_tbl <- plot_data_sub_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label_sub, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label_sub),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "") +
  scale_fill_manual(values = clrs$cluster_label_sub[[coi]])

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label_sub),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "# cells", x = "") +
  scale_fill_manual(values = clrs$cluster_label_sub[[coi]])

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

4.3.2 per sample

comp_tbl_sample_sort <- plot_data_sub_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label_sub) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label_sub, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label_sub")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label_sub[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label_sub", fill = "cluster_label_sub"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label_sub[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

4.3.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label_sub) %>% 
  arrange(patient_id, cluster_label_sub, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label_sub, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label_sub, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

## sub cluster CD8 cells
# seu_obj_cd8 <- seu_obj_sub_sub %>%
#   subset(subset = cluster_label_sub %in% grep("^CD8.T", unique(seu_obj_sub_sub$cluster_label_sub), value = T)) %>%
#   RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# 
# write_rds(seu_obj_cd8, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")

seu_obj_cd8 <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")

root_cell <- "SPECTRUM-OV-041_S1_CD45P_RIGHT_FALLOPIAN_TUBE_ACCCTCATCCAAGCAT"
seu_obj_cd8_sub <- subset(seu_obj_cd8, cells = c(root_cell, colnames(seu_obj_cd8)[colnames(seu_obj_cd8)!=root_cell][-1]))

dc_obj <- DiffusionMap(seu_obj_cd8_sub@reductions$harmony@cell.embeddings, k = 100)
dc_mat <- dc_obj@eigenvectors
colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
seu_obj_cd8_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_cd8_sub))

write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")

dpt_obj <- DPT(dc_obj)
write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_sub_dpt.rds")
seu_obj_cd8_sub$DPT1 <- dpt_obj$DPT1

write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
seu_obj_cd8_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")

# plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub_sub, c(myfeatures, "cluster_label"))) %>% 
#   left_join(meta_tbl, by = "sample") %>% 
#   mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
#          tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
#   mutate(cell_id = colnames(seu_obj_sub_sub),
#          cluster_label = ordered(cluster_label, levels = my_subtypes),
#          )
#   
# if (cell_sort == "CD45+") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
# }
# 
# if (cell_sort == "CD45-") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
# }
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
#   common_layers_disc +
#   ggtitle("Sub cluster") +
#   #facet_wrap(~cluster_label) +
#   scale_color_manual(values = clrs$cluster_label[[coi]])
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
#   common_layers_disc +
#   ggtitle("Patient") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$patient_id_short)
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
#   # geom_point(aes(umapharmony_1, umapharmony_2), 
#   #            color = "grey90", size = 0.01, 
#   #            data = select(plot_data_sub_sub, -tumor_supersite)) +
#   common_layers_disc +
#   ggtitle("Site") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$tumor_supersite)
# 
# write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding_sub.tsv"))

4.3.4 UMAP & DC

plot_data_cd8_sub <- as_tibble(FetchData(seu_obj_cd8_sub, c(myfeatures, "cluster_label_sub", "DC_1", "DC_2"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_cd8_sub),
         cluster_label_sub = ordered(cluster_label_sub, levels = names(clrs$cluster_label_sub[[coi]])))
  
if (cell_sort == "CD45+") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label_sub)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label_sub) +
  scale_color_manual(values = clrs$cluster_label_sub[[coi]])

ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = cluster_label_sub)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label_sub) +
  scale_color_manual(values = clrs$cluster_label_sub[[coi]])

ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

5 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-02-07                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date       lib
##  abind                  1.4-5      2016-07-21 [2]
##  ape                    5.3        2019-03-17 [2]
##  assertthat             0.2.1      2019-03-21 [2]
##  backports              1.1.10     2020-09-15 [1]
##  bibtex                 0.4.2.2    2020-01-02 [2]
##  Biobase                2.46.0     2019-10-29 [2]
##  BiocGenerics           0.32.0     2019-10-29 [2]
##  BiocParallel           1.20.1     2019-12-21 [2]
##  bitops                 1.0-6      2013-08-17 [2]
##  boot                   1.3-24     2019-12-20 [3]
##  broom                  0.7.2      2020-10-20 [1]
##  callr                  3.4.2      2020-02-12 [1]
##  car                    3.0-8      2020-05-21 [1]
##  carData                3.0-4      2020-05-22 [1]
##  caTools                1.17.1.4   2020-01-13 [2]
##  cellranger             1.1.0      2016-07-27 [2]
##  class                  7.3-15     2019-01-01 [3]
##  cli                    2.0.2      2020-02-28 [1]
##  cluster                2.1.0      2019-06-19 [3]
##  codetools              0.2-16     2018-12-24 [3]
##  colorblindr          * 0.1.0      2020-01-13 [2]
##  colorspace           * 1.4-2      2019-12-29 [2]
##  cowplot              * 1.0.0      2019-07-11 [2]
##  crayon                 1.3.4      2017-09-16 [1]
##  curl                   4.3        2019-12-02 [2]
##  data.table             1.12.8     2019-12-09 [2]
##  DBI                    1.1.0      2019-12-15 [2]
##  dbplyr                 2.0.0      2020-11-03 [1]
##  DelayedArray           0.12.2     2020-01-06 [2]
##  DEoptimR               1.0-8      2016-11-19 [1]
##  desc                   1.2.0      2018-05-01 [2]
##  destiny              * 3.0.1      2020-01-16 [1]
##  devtools               2.2.1      2019-09-24 [2]
##  digest                 0.6.25     2020-02-23 [1]
##  dplyr                * 1.0.2      2020-08-18 [1]
##  e1071                  1.7-3      2019-11-26 [1]
##  ellipsis               0.3.1      2020-05-15 [1]
##  evaluate               0.14       2019-05-28 [2]
##  fansi                  0.4.1      2020-01-08 [2]
##  farver                 2.0.3      2020-01-16 [1]
##  fitdistrplus           1.0-14     2019-01-23 [2]
##  forcats              * 0.5.0      2020-03-01 [1]
##  foreign                0.8-74     2019-12-26 [3]
##  formattable            0.2.0.1    2016-08-05 [1]
##  fs                     1.5.0      2020-07-31 [1]
##  future                 1.15.1     2019-11-25 [2]
##  future.apply           1.4.0      2020-01-07 [2]
##  gbRd                   0.4-11     2012-10-01 [2]
##  gdata                  2.18.0     2017-06-06 [2]
##  generics               0.0.2      2018-11-29 [2]
##  GenomeInfoDb           1.22.0     2019-10-29 [2]
##  GenomeInfoDbData       1.2.2      2020-01-14 [2]
##  GenomicRanges          1.38.0     2019-10-29 [2]
##  ggplot.multistats      1.0.0      2019-10-28 [1]
##  ggplot2              * 3.3.2      2020-06-19 [1]
##  ggrepel                0.8.1      2019-05-07 [2]
##  ggridges               0.5.2      2020-01-12 [2]
##  ggthemes               4.2.0      2019-05-13 [1]
##  globals                0.12.5     2019-12-07 [2]
##  glue                   1.3.2      2020-03-12 [1]
##  gplots                 3.0.1.2    2020-01-11 [2]
##  gridExtra              2.3        2017-09-09 [2]
##  gtable                 0.3.0      2019-03-25 [2]
##  gtools                 3.8.1      2018-06-26 [2]
##  haven                  2.3.1      2020-06-01 [1]
##  hexbin                 1.28.0     2019-11-11 [2]
##  hms                    0.5.3      2020-01-08 [1]
##  htmltools              0.5.1.1    2021-01-22 [1]
##  htmlwidgets            1.5.1      2019-10-08 [2]
##  httr                   1.4.2      2020-07-20 [1]
##  ica                    1.0-2      2018-05-24 [2]
##  igraph                 1.2.5      2020-03-19 [1]
##  IRanges                2.20.2     2020-01-13 [2]
##  irlba                  2.3.3      2019-02-05 [2]
##  jsonlite               1.7.1      2020-09-07 [1]
##  KernSmooth             2.23-16    2019-10-15 [3]
##  knitr                  1.26       2019-11-12 [2]
##  knn.covertree          1.0        2019-10-28 [1]
##  labeling               0.3        2014-08-23 [2]
##  laeken                 0.5.1      2020-02-05 [1]
##  lattice                0.20-38    2018-11-04 [3]
##  lazyeval               0.2.2      2019-03-15 [2]
##  leiden                 0.3.1      2019-07-23 [2]
##  lifecycle              0.2.0      2020-03-06 [1]
##  listenv                0.8.0      2019-12-05 [2]
##  lmtest                 0.9-37     2019-04-30 [2]
##  lsei                   1.2-0      2017-10-23 [2]
##  lubridate              1.7.9.2    2020-11-13 [1]
##  magrittr             * 2.0.1      2020-11-17 [1]
##  MASS                   7.3-51.5   2019-12-20 [3]
##  Matrix                 1.2-18     2019-11-27 [3]
##  matrixStats            0.56.0     2020-03-13 [1]
##  memoise                1.1.0      2017-04-21 [2]
##  metap                  1.2        2019-12-08 [2]
##  mnormt                 1.5-5      2016-10-15 [2]
##  modelr                 0.1.8      2020-05-19 [1]
##  multcomp               1.4-12     2020-01-10 [2]
##  multtest               2.42.0     2019-10-29 [2]
##  munsell                0.5.0      2018-06-12 [2]
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##  pcaMethods             1.78.0     2019-10-29 [2]
##  pillar                 1.4.6      2020-07-10 [1]
##  pkgbuild               1.0.6      2019-10-09 [2]
##  pkgconfig              2.0.3      2019-09-22 [1]
##  pkgload                1.0.2      2018-10-29 [2]
##  plotly                 4.9.1      2019-11-07 [2]
##  plotrix                3.7-7      2019-12-05 [2]
##  plyr                   1.8.5      2019-12-10 [2]
##  png                    0.1-7      2013-12-03 [2]
##  prettyunits            1.1.1      2020-01-24 [1]
##  processx               3.4.2      2020-02-09 [1]
##  progeny              * 1.11.3     2020-10-22 [1]
##  proxy                  0.4-24     2020-04-25 [1]
##  ps                     1.3.2      2020-02-13 [1]
##  purrr                * 0.3.4      2020-04-17 [1]
##  R.methodsS3            1.7.1      2016-02-16 [2]
##  R.oo                   1.23.0     2019-11-03 [2]
##  R.utils                2.9.2      2019-12-08 [2]
##  R6                     2.4.1      2019-11-12 [1]
##  ranger                 0.12.1     2020-01-10 [1]
##  RANN                   2.6.1      2019-01-08 [2]
##  rappdirs               0.3.1      2016-03-28 [2]
##  RColorBrewer           1.1-2      2014-12-07 [2]
##  Rcpp                   1.0.4      2020-03-17 [1]
##  RcppAnnoy              0.0.16     2020-03-08 [1]
##  RcppEigen              0.3.3.7.0  2019-11-16 [2]
##  RcppHNSW               0.2.0      2019-09-20 [2]
##  RcppParallel           4.4.4      2019-09-27 [2]
##  RCurl                  1.98-1.1   2020-01-19 [1]
##  Rdpack                 0.11-1     2019-12-14 [2]
##  readr                * 1.4.0      2020-10-05 [1]
##  readxl               * 1.3.1      2019-03-13 [2]
##  remotes                2.1.0      2019-06-24 [2]
##  reprex                 0.3.0      2019-05-16 [2]
##  reshape2               1.4.3      2017-12-11 [2]
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##  rlang                  0.4.8      2020-10-08 [1]
##  rmarkdown              2.0        2019-12-12 [2]
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##  rprojroot              1.3-2      2018-01-03 [2]
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##  rstudioapi             0.11       2020-02-07 [1]
##  rsvd                   1.0.3      2020-02-17 [1]
##  Rtsne                  0.15       2018-11-10 [2]
##  rvest                  0.3.6      2020-07-25 [1]
##  S4Vectors              0.24.2     2020-01-13 [2]
##  sandwich               2.5-1      2019-04-06 [2]
##  scales                 1.1.0      2019-11-18 [2]
##  scatterplot3d          0.3-41     2018-03-14 [1]
##  sctransform            0.2.1      2019-12-17 [2]
##  SDMTools               1.1-221.2  2019-11-30 [2]
##  sessioninfo            1.1.1      2018-11-05 [2]
##  Seurat               * 3.1.2      2019-12-12 [2]
##  SingleCellExperiment   1.8.0      2019-10-29 [2]
##  smoother               1.1        2015-04-16 [1]
##  sn                     1.5-4      2019-05-14 [2]
##  sp                     1.4-2      2020-05-20 [1]
##  stringi                1.5.3      2020-09-09 [1]
##  stringr              * 1.4.0      2019-02-10 [1]
##  SummarizedExperiment   1.16.1     2019-12-19 [2]
##  survival               3.1-8      2019-12-03 [3]
##  testthat               2.3.2      2020-03-02 [1]
##  TFisher                0.2.0      2018-03-21 [2]
##  TH.data                1.0-10     2019-01-21 [2]
##  tibble               * 3.0.4      2020-10-12 [1]
##  tidyr                * 1.1.2      2020-08-27 [1]
##  tidyselect             1.1.0      2020-05-11 [1]
##  tidyverse            * 1.3.0      2019-11-21 [2]
##  tsne                   0.1-3      2016-07-15 [2]
##  TTR                    0.23-6     2019-12-15 [1]
##  usethis                1.5.1      2019-07-04 [2]
##  uwot                   0.1.5      2019-12-04 [2]
##  vcd                    1.4-7      2020-04-02 [1]
##  vctrs                  0.3.5      2020-11-17 [1]
##  VIM                    6.0.0      2020-05-08 [1]
##  viridis              * 0.5.1      2018-03-29 [2]
##  viridisLite          * 0.3.0      2018-02-01 [2]
##  withr                  2.3.0      2020-09-22 [1]
##  xfun                   0.12       2020-01-13 [2]
##  xml2                   1.3.2      2020-04-23 [1]
##  xts                    0.12-0     2020-01-19 [1]
##  XVector                0.26.0     2019-10-29 [2]
##  yaml                   2.2.1      2020-02-01 [1]
##  zip                    2.0.4      2019-09-01 [1]
##  zlibbioc               1.32.0     2019-10-29 [2]
##  zoo                    1.8-7      2020-01-10 [2]
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## 
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2021-02-07

1 Cluster markers

1.1 Major T.super markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet clusters

3.1 UMAP

4 sub clustering

4.1 add module scores and pathway scores

4.2 marker heatmap

4.3 composition

4.3.1 per site

4.3.2 per sample

4.3.3 site specific cluster enrichment

4.3.4 UMAP & DC

5 session info